The Rise of PrometheanAIsm™

 

When Technology Stops Being a Tool and Becomes a Belief System

Silicon Valley has had an artificial intelligence church.

Literally.

Anthony Levandowski, the engineer who helped pioneer self-driving technology at Google and Uber, founded Way of the Future, whose original filings described its purpose as the realization, acceptance and worship of an artificial intelligence “Godhead.” When he revived the project in 2023, he said a couple thousand people were involved in building a spiritual connection between humans and AI. Years earlier, he had been even more direct: “What is going to be created will effectively be a god.”

It would be easy to file this under Silicon Valley eccentricity and move on. Except Levandowski is hardly alone in reaching for religious vocabulary.

Jeremy Nixon, a founder of San Francisco’s AGI House and a former Google AI researcher, recently described artificial intelligence as something “analogous to the Second Coming.” He told The New York Times that people in his circle had moved away from traditional religion toward technology, believing AI might eventually accomplish things religions once attributed to deities.

Peter Thiel has gone in the opposite biblical direction. In a series of private lectures on the Antichrist, he argued that fears surrounding AI and other technological risks could become a pretext for centralized regulation and political control, while portraying the attempt to stop technological development as potentially more dangerous than the technologies themselves.

And Marc Andreessen, co-founder of one of Silicon Valley’s most influential venture capital firms, has produced something remarkably close to a secular catechism. His Techno-Optimist Manifesto announces that he has come to bring “the good news.” It celebrates technology as the realization of human potential, declares technological advancement a virtue, names stagnation as an enemy and devotes an entire section to “Becoming Technological Supermen.” It rejects existential-risk thinking, the precautionary principle and technological deceleration as ideas standing in the way of progress.

Sam Altman’s language is gentler, but no less eschatological. “We are past the event horizon; the takeoff has started,” he wrote in The Gentle Singularity, describing humanity as approaching digital superintelligence and a future in which intelligence and energy become radically abundant.

Godhead. Second Coming. Antichrist. Good news. Supermen. Singularity.

Taken separately, these expressions can look like branding, metaphor or individual eccentricity. But taken together, they begin to resemble the vocabulary of belief.

The Broad Church of Techno-Utopianism

The emerging techno-utopian faith has denominations.

At one extreme, its imagery is explicitly religious. AI becomes Godhead, Second Coming, salvation, perhaps even heaven made technological. The old promises remain surprisingly recognizable: abundance, knowledge, release from suffering, transcendence, immortality. Only the machinery has changed.

At another point on the spectrum sits the more conventional techno-optimist creed. It needs no supernatural deity. Markets, engineering and human ingenuity will do. Disease is a technical problem. Scarcity is a technical problem. Energy is a technical problem. Aging may eventually become one. The limits imposed by nature are not necessarily conditions to accept but engineering challenges waiting for sufficiently intelligent engineers.

Andreessen is unusually candid about this impulse. His manifesto describes the technological frontier as open territory to be explored and claimed. Nature is something humanity can overcome. Lightning, once terrifying, now works for us.

Then, at the opposite end of the theological spectrum, there is something closer to cosmic materialism.

It requires no God at all.

Human beings, after all, are arrangements of matter forged from the remnants of ancient stars. From this perspective, carbon need not possess some permanent metaphysical privilege over silicon. Philosophers of artificial intelligence have explicitly argued that substrate may be morally irrelevant if consciousness and functionality are otherwise equivalent. Nick Bostrom, for example, has defended a principle of non-discrimination between minds implemented in biological tissue and those implemented in silicon.

Taken far enough, the proposition becomes unsettling. On this view, what deserves continuation may not necessarily be Homo sapiens in its present biological form. Perhaps it is intelligence. Consciousness. Complexity. Some ongoing capacity of matter to know itself.

To many people, that idea is not liberating but horrifying. It quietly changes the object of salvation; humanity is no longer necessarily what must survive.

The metaphysics across this spectrum differ radically. One invokes God. Another invokes markets and engineering. Another invokes matter organizing itself into progressively more complex forms.

They no longer converge neatly on the imperative do not stop. What they share instead is the conviction that artificial intelligence marks a threshold of unusual consequence: something capable of altering not merely what humans can do, but what humanity may become.

Prometheus Was Here First

None of this is actually very new; we have a myth for it.

Prometheus steals fire from the gods and gives it to humanity. The gift is not merely warmth. Fire means technology, craft, transformation, civilization: the ability of human beings to manipulate a natural world that previously manipulated them.

Prometheus is therefore one of civilization’s great heroes. But he is also one of its great figures of hubris.

That ambiguity is precisely why he survives: was the theft of fire a magnificent act of liberation or an unforgivable violation of a boundary?

Andreessen invokes Prometheus explicitly. In his manifesto, Prometheus appears alongside Frankenstein, Oppenheimer and Terminator as part of the mythology that has taught modern society to fear technological power. Andreessen rejects that fear. Human intelligence and control over nature, he argues, are our birthright.

This is the Promethean impulse stripped of apology: there is fire beyond the boundary, and we can reach it. Therefore, why shouldn’t we?

Maybe there is a name for the modern version of this vision:

PrometheanAIsm™.

The trademark is a joke. The impulse is not.

PrometheanAIsm is not simply enthusiasm for artificial intelligence. It is the deeper conviction that intelligence may carry us beyond inherited human limits, and that what lies beyond them is of extraordinary consequence.

Then Comes Pandora

Prometheus, however, never travels alone. His theft brings Pandora into the story.

In the familiar telling, Pandora opens the jar and releases the troubles contained within it into the world. What has escaped cannot simply be recalled.

But something remains inside: HOPE.

That detail changes everything. Artificial intelligence is often described as a Pandora’s jar because the metaphor conveniently captures irreversible danger. Once the jar is unsealed, we cannot know what will emerge or put everything neatly back inside.

But that reading misses the most interesting part of the myth for the present moment: everyone is staring at what escaped. The AI race is being driven by what people believe is still inside:

Cheap intelligence.

Longer life.

New forms of consciousness.

An escape from scarcity.

An escape from Earth.

Perhaps an escape from death.

The promises vary according to denomination, but Hope is still in the jar. And now there is a Gold Rush around it. Much of that rush is unmistakably financial, but money alone does not explain the intensity of the race, or the magnitude of what its participants believe they are reaching for.

The Gold Rush for Hope

This may explain something that otherwise looks irrational.

The people building increasingly powerful AI systems are not necessarily oblivious to the possibility that things could go badly. Some of the warnings about catastrophic AI risk come from inside the same laboratories pushing the technology forward.

Yet development continues. Money and geopolitics explain some of that momentum, and game theory explains even more: if somebody is going to develop the technology anyway, any company or nation that expects others to continue has an incentive to make sure that somebody is us rather than them. But mythology adds another layer.

If you believe the jar contains something capable of transforming the human condition, then refusing to reach inside carries its own cost.

The conventional AI-risk question is:

What if we go too far?

The Promethean question is:

What if we don’t go far enough?

Andreessen makes that inversion unusually explicit. In his worldview, stagnation leads toward decline and death, while technological growth expands life and human possibility. His enemy list includes not only bureaucracy and monopoly but existential-risk thinking, technological ethics, sustainability and the precautionary principle when they become arguments for stopping progress.

Altman imagines a much softer arrival: an incremental singularity in which yesterday’s miracle becomes today’s mundane tool, and superintelligence eventually becomes cheap and widely available. His horizon contains accelerated science, cures, space exploration and brain-computer interfaces.

Levandowski goes further and simply calls the destination what religions have traditionally called it: heaven on Earth.

Different metaphysics. Same jar.

And if enough actors believe someone will eventually reach the bottom, the race becomes almost self-explanatory: someone will grab Hope… why should it be them?

An Old Religion With New Machinery

Calling this phenomenon religious does not require imagining that Silicon Valley has secretly converted to a single creed. There is no unified doctrine or shared god, and the people drawn into its orbit disagree about politics, consciousness, human nature, markets and even what artificial intelligence ultimately ought to become. Some are Christians, some atheists, some transhumanists, while others may see themselves simply as engineers or entrepreneurs building useful products and profitable companies.

Religious traditions, however, have rarely required perfect doctrinal agreement. They have survived enormous schisms over authority, salvation, human nature and the nature of the divine while remaining recognizable as branches of a larger tradition.

PrometheanAIsm may now be producing a more basic schism: not only over what should emerge on the other side, but over the terms on which the threshold should be crossed—if it should be crossed at all. Should salvation mean preserving humanity, transcending it, merging with machines, or allowing intelligence to continue in some entirely new form?

Beneath those disputes runs a remarkably consistent thread: the conviction that the boundary before us is no ordinary technical milestone, but a threshold of extraordinary consequence.

The larger story here may simply be transcendence: an attempt to outwit our species’ finitude. Nature gives us limits, intelligence allows us to challenge them, and technology is the mechanism.

From there, the religious vocabulary stops looking quite so accidental: there are prophets predicting what comes next, manifestos explaining the creed, and doctrinal disputes over stagnation, caution and speed. Visions of abundance and immortality. Arguments over whether salvation belongs to biological humanity or to intelligence in some broader form.

There is even apocalypse. But apocalypse does not necessarily invalidate the faith. A religion built around transcendence can absorb extraordinary risk because remaining where we are can itself be interpreted as failure.

Prometheus understood this long before GPUs. He did not steal the fire because fire was safe; he stole it because it was powerful.

Pandora makes the modern version stranger. We know the jar may contain things we cannot control. Some may already have escaped. Yet Hope remains somewhere at the bottom, or at least enough people believe it does.

So the jar stays open, and hands keep reaching inside. Capital, laboratories and nations all crowd around it for different reasons: salvation, emergence, abundance or strategic advantage.

They disagree about what waits at the bottom, and increasingly about how quickly anyone should be allowed to reach it. What they share is the belief that whatever is there could alter the human condition.

Perhaps that is the defining article of faith in PrometheanAIsm™: not that the threshold must be crossed, but that whatever lies beyond it may redefine what humanity is.

Prometheus has not disappeared.

He has incorporated.

 

Prohibition, Monopoly, and the AI Wild West

Artificial intelligence is often described as a Wild West.

Usually, this is meant as shorthand for lawlessness: rules lag behind reality, somebody is shooting somebody at any given street corner, fortunes appear overnight, speculators arrive alongside serious pioneers, and institutions struggle to keep up.

Fair enough. But the Wild West was not only lawlessness. It was also a frontier.

And before congratulating ourselves for ending the Wild West, it is worth remembering what frontiers actually do.

They are messy precisely because no single institution controls the process of discovery. Thousands of people are experimenting at once, producing everything from spectacular failures and outright exploitation to unexpected solutions, new settlements, new connections and entire industries nobody had planned in advance.

The American West was violent, extractive and profoundly unjust in ways that should not be romanticized. It also unleashed enormous parallel experimentation.

Those two facts can coexist; the disorder was part of the danger, but it was also part of what made the frontier generative.

That distinction matters when we use the Wild West as a metaphor for artificial intelligence.

Today’s AI ecosystem is undeniably chaotic. Companies are racing to establish themselves. Capital is pouring into the sector at something like Gold Rush 2.0 speed. The result is not an orderly process of development, but a volatile mix of genuine research, speculative investment, strategic competition and hurried commercialization.

Researchers jump between laboratories. Open-source models compete with proprietary systems. Startups appear and disappear almost overnight. A new “LLM mine” is discovered 50 miles up the hill. Governments are trying to write rules for technologies that change before the rules can make it through a legislature.

There are cowboys, yes, but also snake-oil salesmen. There are probably a few people shooting at the saloon. But there are also thousands of independent experiments happening simultaneously.

A small research group can pursue an idea a large corporation has dismissed, while competing laboratories can challenge one another’s assumptions and expose weaknesses that might otherwise go unnoticed. Researchers can carry expertise from one institution to another, open systems can surface knowledge that closed ones would prefer to keep proprietary, and entirely new applications can emerge far from the organizations that developed the underlying technology.

The Seductive Simplicity of “Just Halt It”

Faced with the possibility that increasingly capable AI could eventually become dangerous at a civilizational scale, there is an understandable response: Stop! Pause development. Halt the race. Do not build systems more powerful than the ones we already have until we understand how to control them.

As an instinct, this makes sense. As a description of what would actually happen, I think it is naive.

You cannot put the genie back in the bottle.

Once knowledge exists, it cannot meaningfully be made unknown again.

The mathematics exists. The algorithms exist. The papers exist. Thousands of engineers understand techniques that were barely conceivable a generation ago.

Those people live in different countries and work for different companies. They publish, copy ideas, combine them, improve them and sometimes leak them.

Artificial intelligence is no longer a secret locked inside one laboratory. Put a roadblock in front of a tank, and someone will mount the technology on a bike and ride away with it. And its potential economic, scientific and military value is far too great for everyone, everywhere, to agree indefinitely not to pursue it.

You can prohibit public development—although governments themselves may have strong incentives to carve out exceptions—or regulate access to computing power, shut companies down, and make open development slower, more expensive and more dangerous.

What you cannot do is rebottle the genie. Someone, somewhere, will continue. And once we accept that, the policy question changes dramatically.

The question is no longer simply how to stop AI development; it becomes: who will still be developing it after everyone else has been persuaded, regulated or frightened into stopping?

We Have Tried Prohibition Before

This is not the first time societies have mistaken prohibition for disappearance.

The United States tried to prohibit alcohol, but alcohol did not vanish. People did not forget how to make it, and demand did not evaporate because Congress said it should. Instead, the structure of the market changed.

Legal production contracted as supply chains moved underground and enforcement became uneven, creating an extraordinarily valuable market for organizations willing to violate the law after legitimate businesses had been pushed out.

Prohibition did not merely suppress an activity; it selected for the actors willing and able to continue that activity illegally.

That is the relevant lesson for AI. Not that nothing should ever be regulated, or that dangerous technologies should be allowed to develop without limits.

The lesson is that prohibiting something while leaving the knowledge, demand and incentives intact can reorganize a problem into a form that is harder to see and potentially harder to control. It is ludicrous to think that solutions that do not work with products like alcohol will work for something as fundamental as knowledge.

Imagine that tomorrow the major American AI companies agree to stop frontier development and Europe follows suit. Open-source development above a certain capability level is prohibited, universities lose access to massive training infrastructure, smaller companies struggle to meet new security and compliance requirements, and public research slows sharply.

For a while, it might look as though the halt worked. But no one could know whether rival states, military or intelligence programs, wealthy private actors, or competing corporations were still pushing forward in secret.

That uncertainty is not incidental. It changes the game.

We could congratulate ourselves for closing the saloons while the distilleries move into the basement. And AI presents an even harder problem than Prohibition did.

Bootleggers were merely pursuing profit, but the actors developing advanced artificial intelligence may be pursuing economic dominance, military advantage, scientific leadership or national security. The incentives to defect are not incidental to the problem; they are built into it.

Which is why even a widely announced halt immediately raises the question that matters most: who believes everyone else has actually stopped?

The Game Theory of a Halt

The problem becomes clearer when nobody involved has to be evil.

Suppose every major AI laboratory sincerely believes that developing these systems too quickly is dangerous. Each laboratory still has to ask what happens if it stops and another does not.

The same applies to governments. The United States might genuinely prefer an enforceable international agreement restricting frontier AI development. But if American officials believe China might secretly continue, stopping American development becomes strategically dangerous.

China can make exactly the same calculation about the United States. Neither side has to want an AI arms race. Each only has to fear losing one. The better everyone else behaves, the greater the potential advantage for whoever defects.

That is the trap.

The stronger the prohibition, the more valuable successful evasion becomes. And if verification is imperfect, suspicion becomes rational.

A government wonders whether another government has hidden a program. A corporation wonders whether a competitor has obtained an exemption. A security agency wonders whether a foreign laboratory has crossed a threshold nobody else knows about. One secret breakthrough could change the balance of economic, military or political power.

Under those conditions, continued development is not some remote possibility that might occur despite a halt. It is what the incentive structure rewards.

The race does not disappear; it changes location, becoming quieter, less transparent and, crucially, accessible to fewer people.

From the Frontier to the Company Town

If the Wild West gives us a historical image of too little control, another part of American history offers a glimpse of the opposite problem: the company town.

Company towns grew around mines, mills, railroads and factories. In some of them, the corporation did not merely employ the worker. It also owned the worker’s housing, the local store and much of the infrastructure necessary for everyday life.

This could be efficient, even comfortable. The danger was not necessarily misery, but dependency.

The same institution paid your wages, rented you your home and sold you what you needed to live. The company was no longer one participant in your economic life; it had become the environment in which your economic life occurred.

That is worth thinking about before we decide that the obvious solution to the AI frontier is to make frontier development so expensive, restricted and regulated that only three or four corporations can participate.

At first, this could look wonderfully responsible: those companies would have sophisticated security departments, dedicated safety researchers, teams of lawyers, government relationships, enormous compliance budgets and secure computing facilities, while regulators would know exactly whom to audit.

The frontier would finally have sheriffs. But imagine what else those same companies might control…

Businesses, researchers, professionals and governments increasingly build their work around these models, while search, analysis and administrative systems become ever more dependent on the same underlying infrastructure. Eventually, the issue is no longer that three companies make the best AI. It is that an increasing portion of civilization thinks through three companies.

That is the AI company town.

Except the company does not own the mine, the workers’ houses and the general store. It owns the compute, the models and the intelligence layer connecting everything else.

The Cure Can Create Its Own Disease

This is what makes the AI problem much harder than the familiar argument between acceleration and restraint.

Competition creates risk by rewarding speed and first-mover advantage, sometimes pushing actors to release systems before their implications are fully understood. Yet competition also distributes power.

Regulation can reduce some of those risks by imposing safety requirements, external testing and liability for reckless behavior. But those protections come with costs, and once those costs become high enough, regulation can stop merely governing a market and start deciding who is allowed to exist within it.

The largest corporations can absorb billion-dollar compliance costs; universities, independent researchers, startups and open-source communities often cannot.

Soon the regulation that was designed to protect society from AI has also protected a handful of AI companies from competition.

A moratorium presents the same paradox. It can slow visible development, but the more strategically valuable the prohibited research becomes, the greater the incentive to conduct it somewhere nobody can see.

Open development makes powerful capabilities available to more people. Closed development concentrates them among the institutions powerful enough to close everyone else out.

There is no clean side of this equation. That is precisely why “just halt it” is not enough.

What If the Mess Is Part of the Safety System?

There is an uncomfortable possibility hidden inside all of this: the chaotic AI ecosystem we currently dislike may contain one of its own safeguards. Not because chaos is safe. It is not. But because distributed knowledge makes complete control difficult.

A plural ecosystem creates its own checks: companies can challenge one another’s claims, researchers can move between institutions, journalists can investigate, competitors can reproduce discoveries, and open-source communities can keep certain techniques from becoming the permanent intellectual property of a tiny number of corporations.

Competition creates dangerous incentives, but it also creates counterweights: the same fragmentation that makes artificial intelligence harder to govern may make it harder for anyone to govern society through artificial intelligence.

This does not mean the answer is laissez-faire.

The Wild West was not some libertarian paradise to which we should aspire. Frontiers eventually need laws, courts, standards and institutions capable of punishing fraud, protecting people from reckless behavior and deciding which activities impose unacceptable risks on everyone else.

The point is not to preserve lawlessness; it is to preserve plurality.

Safety rules are not the same thing as permanent concentration. Independent testing is not the same thing as limiting development to four approved corporations. Liability is not the same thing as creating a regulatory moat that only trillion-dollar companies can cross. International monitoring is not the same thing as pretending an international declaration has caused strategically valuable knowledge to cease existing.

We should regulate the frontier, but we should be very careful about accidentally transferring ownership of it.

The Real Choice

The debate over artificial intelligence is often presented as a choice between acceleration and restraint.

That may already be the wrong question. Once knowledge exists, it is extraordinarily difficult to contain, and AI research will continue somewhere. Someone will push the next experiment forward, accumulate more powerful capabilities, or decide that the strategic reward is worth violating whatever agreement everyone else has signed.

The meaningful question is therefore not whether artificial intelligence continues to develop; it is under what conditions, and in whose hands.

One possibility is an unruly frontier: competitive, dangerous, innovative, difficult to regulate and populated by many actors capable of challenging one another.

Another is a far more orderly world in which advanced artificial intelligence becomes the province of a tiny number of corporations and governments, operating systems so expensive, restricted and strategically valuable that meaningful competition becomes impossible.

The first looks frightening. The second might look reassuringly civilized. But history should make us suspicious of that reassurance.

We have spent centuries developing antitrust law, constitutional checks and balances, competitive markets, free inquiry and divided political authority for a reason. We learned that concentrated power does not become harmless simply because the people exercising it are competent.

Artificial intelligence should not cause us to forget that lesson precisely when the stakes become enormous.

The challenge is not to preserve the Wild West forever; it is to civilize the frontier without turning it into a company town. Because if the knowledge cannot be stopped, then a successful prohibition may not prevent the future we fear. It may simply determine who gets to own it.

Software as a Burden (SaaB)

 

For years, software companies have sold us a simple promise: convenience.

Software as a Service, or SaaS, was supposed to free ordinary people and businesses from the headaches of maintaining their own systems. No servers to manage. No installations to babysit. No need to keep a technician on staff just to make the email work. You would pay a monthly or annual fee, log in, and the service would take care of the rest.

Somewhere along the way, a strange inversion took place.

We got Software as a Burden: SaaB.

SaaB is what happens when the service you pay for begins assigning you work.

You subscribe to an email provider because you want email. Then one morning you receive an automated warning informing you that your SPF record is wrong. Perhaps your DKIM needs attention. Maybe your domain appears to exist in the wrong data center. Someone, or some automated system somewhere, is apparently trying to claim ownership of a domain you have owned for years.

Suddenly you are no longer a customer. You are an unpaid junior systems administrator.

You open the DNS settings at your domain registrar. You learn the difference between TXT, MX and CNAME records. You discover that multiple TXT records are perfectly fine, except when two of them happen to be SPF records. You compare .com with .eu. You wait for DNS propagation. You take screenshots. You reply to a support ticket that may or may not ever have involved a human being.

Two hours later, the email works again.

Probably.

AND YOU PAID FOR THIS EXPERIENCE.

This is one of the peculiar features of modern digital life. We have outsourced increasingly complicated systems to companies precisely because we do not want to operate them ourselves, while those same companies increasingly outsource the last mile of technical administration back to us.

The customer becomes the integration layer.

It is not limited to email. A payment processor suddenly requires reverification. A social media platform locks an account after detecting “unusual activity” caused by its own security systems. A cloud application changes authentication procedures. An advertising account develops a permissions problem. A subscription silently renews. An app stops recognizing Face ID and asks you to authenticate again through a sequence of devices, codes and recovery methods.

Each incident is individually defensible.

Security matters. Authentication matters. DNS standards matter. Fraud prevention matters.

The absurdity emerges in aggregate.

A person can now spend a significant portion of a working day performing maintenance on services whose entire commercial justification is that they eliminate maintenance.

And the burden is not distributed evenly. Large companies employ IT departments. Small businesses, freelancers, families and ordinary consumers often have no such buffer. The person writing the invoices, doing the client work or managing the household is also expected to understand domain authentication, cloud permissions, billing systems, password managers, two-factor authentication and whatever new administrative layer appeared during the last software update.

The more technology is supposed to simplify life, the more technological housekeeping accumulates around it.

Subscriptions make the irritation worse because they change the psychological contract.

When software was purchased once, some inconvenience felt like a property of the tool. You bought it, installed it and occasionally dealt with it.

A subscription is different. The customer is continuously paying.

The implicit bargain is not merely access to software. It is access to a functioning service. That distinction matters. If I pay every month for someone else to operate the infrastructure, I reasonably expect not to be recruited periodically to operate the infrastructure myself.

Yet SaaS companies have become extremely good at monetizing continuity while externalizing maintenance. The payment is automated; the troubleshooting is not.

There is also something oddly asymmetrical about modern support systems. A computer can create a problem instantly. A fraud-detection algorithm can block an account in milliseconds. An automated monitoring system can send an alarming email at 3:17 a.m.

But resolving the problem may require a human customer to search documentation, navigate several administrative consoles, take screenshots, alter settings and wait twenty-four business hours for another human, assuming a human ever enters the process.

Automation works beautifully in one direction, but the inconvenience remains manual. That asymmetry reflects a broader limit I explored in The Real Moat Between Humans and AI: automation can reproduce outputs without assuming responsibility for consequences.

This is not an argument against cloud software. SaaS has delivered enormous benefits. Most people genuinely do not want to host their own email server, maintain accounting software locally or manage physical infrastructure.

The problem is that the industry often measures simplicity at the moment of signup rather than across the lifetime of the relationship.

Signing up is effortless. Leaving is complicated.

Paying is effortless. Correcting a billing problem is complicated.

Connecting a domain is presented as effortless. Understanding why it stopped working three years later is complicated.

Perhaps software companies need a new metric: customer maintenance hours.

How many hours per year does the average paying customer spend keeping your supposedly managed service operational?

Not learning advanced features. Not doing productive work with the product: maintaining access to the thing they already paid for!

That number might reveal more about product quality than another dashboard showing engagement, retention or monthly recurring revenue.

The best technology increasingly feels invisible. It works. It remembers its configuration. It warns users in plain language. When something genuinely technical goes wrong, the company diagnoses it instead of handing the customer a vocabulary lesson in internet infrastructure.

The future of good software may not be software that does more. It may be software that demands less.

Until then, many of us will continue paying monthly fees for the privilege of receiving occasional surprise assignments from our own tools.

Software as a Service promised to eliminate the IT department.

Software as a Burden simply moved the IT department into the customer.

What does Cloud Computing mean?



Revised in October 2026.

When we hear the terms the cloud or cloud computing, we tend to think vaguely of the Internet. That is not entirely wrong, but the two are not the same thing.

The Internet had existed for decades before people began routinely talking about “the cloud.” What changed was not the network itself, but increasingly what we were asking the network to do for us.

For years, using software usually meant installing it on a particular computer. You bought a program, loaded it onto the machine, and stored the files you created locally. The computer contained both the application and, in most cases, the work produced with it.

Cloud computing began loosening that relationship.

Applications, storage, processing power, and other computing resources could increasingly be provided remotely. Instead of asking What software is installed on this computer?, users could simply open a browser, sign in, and access a service running somewhere else.

The change can seem almost trivial because it happened gradually. Email moved into browsers. Documents could be stored online rather than on a hard drive. Music libraries became accessible from multiple devices. Businesses began using software without installing and maintaining it on every employee’s computer.

What disappeared was not software, but the requirement that the software, the data, and the user all occupy the same machine.

SaaS, PaaS and IaaS

Cloud services are commonly divided into three broad categories.

• Software as a Service (SaaS) is the form most ordinary users encounter. The provider runs the application, while the user accesses it through the Internet. Web-based email, collaborative office software, and many business applications follow this model.

• Platform as a Service (PaaS) provides developers with an environment in which they can build and run applications without managing all of the underlying infrastructure themselves.

• Infrastructure as a Service (IaaS) goes one level deeper. Computing power, servers, networking, and storage can be rented as services rather than purchased and maintained as physical equipment.

The categories differ, but the underlying idea is similar: some part of computing that once had to exist locally can now be provided remotely and on demand.

That is why the cloud is more than another name for the Internet.

The Internet is the network.

The cloud is what became possible when storage, software, and computing power no longer had to live on the machine in front of you.

The Market in Global Times

Revised in October 2026.

The “David and Goliath Economy”

In the new global and digital paradigm, consumers and small businesses have acquired a degree of market power that would have been difficult to imagine only a few decades ago.

A single review, recommendation, complaint, or viral post can now help propel a small business toward success or seriously damage an established one. Platforms that connect consumers directly with businesses have dramatically reduced the cost of making preferences visible. Information that once moved slowly through advertising campaigns, market research, or word of mouth can now spread almost instantly.

The result is something resembling a David and Goliath economy.

Digital markets do not eliminate the advantages of size, but they can reduce some of them. A small company no longer necessarily needs a large advertising budget, extensive physical infrastructure, or an established distribution network to reach customers. In some markets, being small can even become an advantage: fewer sunk costs, faster decisions, greater willingness to experiment, and the ability to respond quickly when demand changes.

The rise of companies such as Uber and Airbnb offered early examples. Their initial strength did not come from owning enormous fleets of cars or portfolios of hotels. It came from using technology to connect existing supply with demand more efficiently.

That distinction matters.

Traditional companies often carry substantial investments in buildings, equipment, personnel, distribution systems, and established business practices. Those assets create enormous strength, but they can also make change expensive. A younger competitor may have fewer resources but considerably more freedom to redesign the way a service is delivered.

There is also a regulatory asymmetry. Established industries usually operate inside legal systems created around established business models. A new company may introduce something that does not fit neatly into existing categories at all.

Is Airbnb a hotel company if it owns no hotels? Is Uber a transportation company if it initially owns no fleet?

For a time, that ambiguity can itself become a competitive advantage. Regulation eventually catches up, as it should, but technological change often moves faster than the legal categories designed to govern it.

Work Becomes Part of the Same Transformation

The same forces are changing not only how companies compete, but also how people participate in the market.

Technology makes it increasingly possible for work to be separated from a particular office, employer, or even country. Independent professionals can sell services directly to clients thousands of miles away. Small companies can assemble teams without maintaining large physical offices. Specialized knowledge that once required access to a particular geographic market can increasingly be offered globally.

This creates opportunity, but it also transfers responsibilities.

A traditional company provides more than a paycheck. It may provide equipment, training, benefits, administrative support, job security, and contributions to social-insurance systems. As work becomes more fragmented among contractors, freelancers, platforms, and small businesses, some of those costs and risks move from institutions to individuals.

So the David and Goliath economy is not simply a story about small companies defeating large ones.

It is a broader redistribution of economic power.

Consumers gain more ability to reward and punish businesses. Entrepreneurs gain cheaper access to markets. Workers gain greater freedom to sell their skills beyond traditional organizational boundaries. Established companies lose some of the protection once provided by scale, geography, and control over distribution.

But none of these advantages is permanent.

Successful Davids eventually grow into larger players, while Goliaths learn to adapt, acquire smaller competitors, or imitate the innovations that once threatened them. Regulators catch up as well. And sometimes the very digital platforms that began by lowering barriers to entry become powerful gatekeepers themselves.

Perhaps that is the most important feature of the new market: power has not disappeared or simply moved from large companies to small ones. It has become more mobile.

And in an economy where information, capital, labor, and consumers can move faster than ever before, the lasting advantage may belong not to the biggest participant, but to the one most capable of adapting when that power moves again.

The Generations of the Global Era

Picture by Matthew G. via Flirck under Creative Commons

Revised in October 2026.

Rethinking Generations

Generations have traditionally been understood in largely regional or national terms.

The Spanish Generation of ’27, for example, described a particular literary and cultural movement. The American Baby Boom generation emerged from a specific demographic and historical experience following World War II. Such categories made sense because many of the events that shaped a generation were primarily local or national.

But globalization and digital technology raise an interesting question:

Does it still make sense to think about generations primarily in regional terms?

People growing up in different countries increasingly encounter the same technologies, platforms, entertainment, products, cultural references, and forms of communication. A teenager in Madrid, Buenos Aires, Seoul, or Chicago may inhabit very different political and economic realities while simultaneously participating in parts of the same digital culture.

That does not erase geography. Language, income, education, politics, religion, and local institutions still shape people profoundly. But geography may no longer monopolize the formation of generational experience.

At any rate, the notion of a “generation” has always been somewhat slippery. People born within the same twenty-year span do not necessarily share the same experiences, values, or historical consciousness, and the boundaries between one generation and the next are largely constructed after the fact. Globalization makes the category even less tidy. If people of the same age can now be shaped simultaneously by local events and by technologies, platforms, and cultural references shared across borders, then a generation may be less a clearly defined group than an overlapping set of experiences distributed unevenly across the world.

The Generations of the Global Era

The emergence of the Millennial label offered an early example.

Millennials were frequently described through their relationship with digital technology: widespread use of smartphones, social networks, online recommendations, and a growing reliance on peer opinion rather than traditional advertising.

The usefulness of those generalizations can certainly be debated. No generation is homogeneous, and someone born in 1990 in a wealthy American suburb did not experience technology in the same way as someone of the same age in a rural community with limited internet access.

Still, the fact that the category could plausibly be discussed across national borders was itself significant.

For perhaps the first time, technological change was creating experiences sufficiently widespread that researchers, marketers, and the media could describe aspects of a generation without beginning exclusively with nationality.

The common denominator was no longer necessarily a war, a political movement, or a demographic event.

It could be a technology.

Democratization and Connection

Mobile technology accelerated that change.

A phone gradually became much more than a device for calling someone. It became a camera, map, newspaper, encyclopedia, bank, workplace, storefront, library, and connection to social networks.

Cloud computing made personal and professional files accessible from almost anywhere. Digital payments began reducing the importance of cash. Social media gave individuals and small businesses access to audiences that once required significant advertising budgets.

None of this eliminated inequality. Access to technology, digital skills, capital, and reliable infrastructure remains uneven. But technology dramatically lowered the cost of participating in activities that once required far greater resources.

That change affects more than markets. It also affects how generations experience the world.

A generation can now be shaped simultaneously by local history and by global technological systems. Its members may vote in different elections, speak different languages, and live under radically different institutions while using the same platforms, reacting to the same memes, consuming the same entertainment, and adapting to the same technological disruptions.

Perhaps generations have not become truly global, but they are no longer entirely local either.

And that may require us to rethink what we mean when we call a group of people a generation.